CN111371862A - Unmanned vehicle debugging method, device, server and medium - Google Patents
Unmanned vehicle debugging method, device, server and medium Download PDFInfo
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Abstract
The application discloses a method, a device, a server and a medium for debugging an unmanned vehicle, and relates to the technical field of unmanned driving. The method is executed by a server, and the specific implementation scheme is as follows: extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle; and debugging according to the abnormal problem data, and determining the abnormal reason according to the debugging result. According to the method and the device, the abnormal problem data are extracted in a targeted mode through the server, the abnormal reason is determined according to the abnormal problem data, the efficiency of determining the abnormal reason is improved, the problem that a local operating system is inconsistent does not need to be considered, the operation of determining the abnormal reason is simplified, and the cost is saved.
Description
Technical Field
The application relates to the field of automation, in particular to an unmanned technology, and specifically relates to an unmanned vehicle debugging method, device, server and medium.
Background
Functional modules (such as a control module and a path planning module) of automation equipment such as an unmanned vehicle and a robot involve a lot of signals, and after the functional modules are deployed to a certain scale, an abnormal problem occurs, and debugging is needed to determine the reason of the abnormality.
In the current scheme, data files of functional modules in a server are downloaded to local equipment, and debugging is performed on the local equipment, or secondary playback is performed on vehicle operation data in the server.
However, downloading the data file of the functional module to the local is time-consuming and labor-consuming, and operating systems of different devices are different, which requires cross-platform debugging tools. The server performs secondary playback on the vehicle operation data, only vehicle motion modes such as macro signals of speed, acceleration and the like can be displayed, the information amount is small, and the abnormal reason cannot be accurately determined.
Disclosure of Invention
According to the unmanned vehicle debugging method, the unmanned vehicle debugging device, the unmanned vehicle debugging server and the unmanned vehicle debugging medium, the abnormal reason can be accurately determined in the server, and the efficiency of determining the abnormal reason is improved.
The embodiment of the application discloses a method for debugging an unmanned vehicle, which comprises the following steps:
extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and debugging according to the abnormal problem data, and determining the abnormal reason according to the debugging result.
The above embodiment has the following advantages or beneficial effects: the abnormal problem data are accurately extracted according to the abnormal problem information, the detection module is debugged according to the abnormal problem data, and the abnormal reason is determined, so that the problems that the abnormal analysis can be carried out only by downloading the data from the cloud to the local device, the motion condition of the vehicle can be secondarily played by the cloud, and the abnormal reason cannot be accurately determined are solved, the abnormal reason can be accurately determined by the cloud server, and the determination efficiency of the abnormal problem is improved.
Further, the operation data of the module to be detected includes input data, internal state variable data and output data of the module to be detected.
Accordingly, the above-described embodiments have the following advantages or advantageous effects: through the input data, the internal state variable data and the output data of the module to be monitored, the abnormal condition of the module to be monitored can be analyzed more accurately, the detail information of the abnormal reason can be determined, and fine-grained processing at a frame level is supported.
Further, according to the abnormal problem information of the module to be detected in the unmanned vehicle, the abnormal problem data is extracted from the operation data of the module to be detected, which is recorded in advance, and the method comprises the following steps:
determining an abnormal time period and an abnormal data extraction template according to abnormal problem information of a module to be detected in the unmanned vehicle;
and extracting abnormal problem data from the pre-recorded operation data of the module to be detected according to the abnormal time period and the data type in the abnormal data extraction template.
Accordingly, the above-described embodiments have the following advantages or advantageous effects: the data type is determined according to the abnormal time period and the abnormal data extraction template, and the abnormal problem data is further extracted, so that the extracted abnormal problem data is more representative and targeted, and the abnormal reason can be more accurately analyzed through the abnormal problem data.
Further, debugging according to the abnormal problem data comprises:
debugging according to the abnormal problem data through a web technology;
and displaying the debugging result through a visualization tool.
Accordingly, the above-described embodiments have the following advantages or advantageous effects: the module to be detected is debugged according to the abnormal problem data, and the debugging result is displayed through a visual tool, so that the abnormal reason is clearly and directly determined.
Further, debugging according to the abnormal problem data, and determining an abnormal reason according to a debugging result, including:
simulating the running process of the vehicle according to the abnormal input data and the abnormal output data of the module to be detected by the vehicle model, and determining candidate abnormal reasons according to the vehicle simulation data;
and determining a target abnormal reason from the candidate abnormal reasons according to the internal state variable data of the module to be detected by a web technology.
Accordingly, the above-described embodiments have the following advantages or advantageous effects: the candidate abnormal reasons can be preliminarily determined by simulating the running process of the vehicle, the target abnormal reasons can be further debugged according to the internal state variable data, and the target abnormal reasons are selected from the candidate abnormal reasons, so that the specific reasons and details for generating the abnormality can be more accurately analyzed, and the unmanned vehicle can be debugged according to the specific reasons and details.
Further, if the module to be detected is a control module, the input data includes: the output path, the positioning information and the vehicle chassis feedback information planned by the local decision planning module; the output data includes: a steering wheel angle; the internal state variable data includes: the grade value, the disturbance rejection value, the time, the lateral control error, the longitudinal control error, the rate of change of the lateral control error, the course angle error, the rate of change of the course angle error, and the position of the host vehicle.
Further, if the module to be detected is a local decision planning module, the input data includes: positioning data, map data, navigation data, vehicle chassis data, perception module data and prediction module data; the output data comprises a local trajectory; the internal state variable data includes: path planning variables, speed planning variables, and decision planning variables.
Accordingly, the above-described embodiments have the following advantages or advantageous effects: input data, output data and internal state variable data are determined according to two conditions that a module to be detected is a control module and a local decision planning module, so that abnormal conditions of the control module and the local decision planning module are analyzed more pertinently, and abnormal reasons are accurately determined.
The embodiment of the application also discloses unmanned vehicle debugging device, and the device includes:
the abnormal problem data extraction module is used for extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and the abnormal reason determining module is used for debugging according to the abnormal problem data and determining the abnormal reason according to the debugging result.
Further, the operation data of the module to be detected includes input data, internal state variable data and output data of the module to be detected.
Further, the abnormal problem data extraction module includes:
the abnormal information determining unit is used for determining an abnormal time period and an abnormal data extraction template according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and the data extraction unit is used for extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal time period and the data type in the abnormal data extraction template.
Further, the abnormality cause determination module includes:
the debugging unit is used for debugging according to the abnormal problem data through a web technology;
and the debugging result display unit is used for displaying the debugging result through a visual tool.
Further, the abnormality cause determination module further includes:
the preliminary determining unit is used for simulating the running process of the vehicle according to the abnormal input data and the abnormal output data of the module to be detected through the vehicle model and determining candidate abnormal reasons according to vehicle simulation data;
and the reason determining unit is used for determining a target abnormal reason from the candidate abnormal reasons according to the internal state variable data of the module to be detected through a web technology.
Further, if the module to be detected is a control module, the input data includes: the output path, the positioning information and the vehicle chassis feedback information planned by the local decision planning module; the output data includes: a steering wheel angle; the internal state variable data includes: the slope value, the disturbance rejection value, the time, the transverse control error, the longitudinal control error, the transverse control error change rate, the course angle error change rate and the main vehicle position;
if the module to be detected is a local decision planning module, the input data includes: positioning data, map data, navigation data, vehicle chassis data, perception module data and prediction module data; the output data comprises a local trajectory; the internal state variable data includes: path planning variables, speed planning variables, and decision planning variables.
The embodiment of the present application further discloses a server, which includes:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as described in any one of the embodiments of the present application.
Also disclosed in embodiments herein is a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of the embodiments herein.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flow chart of an unmanned vehicle debugging method provided according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of another unmanned vehicle debugging method provided according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an unmanned vehicle debugging device provided according to an embodiment of the application;
fig. 4 is a block diagram of a server for implementing the unmanned vehicle commissioning method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic flow chart of an unmanned vehicle debugging method provided according to an embodiment of the present application. The embodiment is applicable to the debugging of the unmanned vehicle. Typically, the present embodiment may be applied to a case where a cause of an abnormality generated during the operation of the unmanned vehicle is determined and analyzed. The unmanned vehicle debugging method disclosed by the embodiment can be executed by a server, and particularly can be executed by an unmanned vehicle debugging device which can be realized by software and/or hardware. Referring to fig. 1, the unmanned vehicle debugging method provided in this embodiment includes:
and S110, extracting abnormal problem data from the operation data of the module to be detected, which are recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle.
The unmanned vehicle may be an unmanned automatic device, such as an automatically moving vehicle, a robot, or the like. The module to be detected can be a functional module in an unmanned vehicle, such as a control module, a local decision planning module and the like. The control module is used for outputting control commands such as steering brake and control by using different control algorithms based on the output path planned by the decision and the state of the vehicle body. The local decision planning module is used for calculating a proper track for the vehicle to run in a limited time range according to the navigation information and the current state of the vehicle. The module to be detected can also be a determination module, a prediction module, a global planning module and the like. The operation data of the module to be detected may include input data, internal state variable data, output data, and the like of the module to be detected. For example, when the module to be detected is a control module, taking a control command as an example of steering, the input data may include an output path planned by the local decision planning module, positioning information, and vehicle chassis feedback information, such as a vehicle speed, steering, an accelerator, and the like; the output data includes: a steering wheel angle; the internal state variable data includes: and key debugging information such as gradient value, disturbance resistance value, time, transverse control error, longitudinal control error, transverse control error change rate, course angle error change rate, main vehicle position and the like. When the control command of the control module is brake control, the input data, the output data and the internal state variable data may also be other key debugging information, which is not specifically limited herein. When the module to be detected is a local decision planning module, the input data includes: positioning data, map data, navigation data, vehicle chassis data, perception module data and prediction module data; the output data comprises a local track, wherein the local track not only comprises a driving route of the unmanned vehicle, but also comprises vehicle speed, acceleration, direction steering information and the like at each moment; the internal state variable data includes: path planning variables, speed planning variables, and decision planning variables.
In the running process of the unmanned vehicle, each module to be detected may have abnormal conditions, and the reason of the abnormal conditions needs to be analyzed and determined. At present, in the running process of an unmanned vehicle, in order to save local storage space, running data of each module to be detected is stored in a cloud server, when the module to be detected is abnormal and needs to analyze the reason of the abnormality, the stored running data is downloaded from the cloud server to a local system, then an independent analysis tool is run on the corresponding local system, and the running data is analyzed to determine the reason of the abnormality. Since each local system is different, the analysis tool needs to adapt to different local systems and has cross-platform performance, and therefore, an analysis tool compatible with each local system needs to be developed, and a developer needs to select an analysis tool suitable for the local system from among a plurality of versions of the analysis tool. In addition, due to the problem of bandwidth stability, the downloading time of the operation data cannot be consistent, and the problem causes low efficiency and high cost of abnormal reason analysis. At present, a scheme for playing back and displaying cloud running data exists, the scheme can only macroscopically display the motion mode of an unmanned vehicle, and specific details which cause problems cannot be determined. Based on the above problems, in the embodiment of the present application, the determination of the abnormal cause is deployed in the server to be completed, the running data does not need to be downloaded to the local, the problem analysis tools under different operating systems are not needed, and the problem of versions of different analysis tools is considered, so that the analysis work of the abnormal cause of the present application can be completed in the server, a large number of intermediate links are omitted, and the efficiency of determining the abnormal cause is improved
In the embodiment of the application, when the module to be detected is abnormal, the reported abnormal problem of the module to be detected is obtained, the abnormal time point of the abnormal problem is determined, and abnormal problem data related to the abnormal problem is extracted from the operation data of the module to be detected, so that the abnormal reason is analyzed according to the abnormal problem data. The reported target time period may also be obtained, and abnormal problem data is extracted from the pre-recorded data in the target time period for subsequent analysis. The operation data of the pre-recorded module to be detected may be stored in a record format or in a log by using a system framework of an open source automatic driving system Apollo (Apollo).
In the embodiment of the application, in order to increase the recording efficiency of data and reduce the space occupied by data, the index of the running data can be recorded only in the server without repeatedly recording the content of the running data, and accordingly, in the debugging stage, the data content can be acquired from the data storage module according to the data index, so that the data acquisition efficiency and the response speed are improved.
And S120, debugging according to the abnormal problem data, and determining an abnormal reason according to a debugging result.
Illustratively, the server analyzes and determines the abnormal reason according to the abnormal problem data to determine the specific reason causing the abnormality of the module to be detected, so that the abnormal reason can be analyzed without downloading the running data to a local system. Moreover, the whole analysis process of the abnormal reasons is completed by the server, and different version analysis tools under different operating systems do not need to be maintained, so that the problem analysis efficiency is improved.
In the embodiment of the present application, the debugging according to the abnormal problem data includes: debugging according to the abnormal problem data through a web technology; and displaying the debugging result through a visualization tool.
Illustratively, by adopting web technology, abnormal problem data is loaded and displayed, and when the abnormal problem data is displayed, the server loads operation data which generates an abnormal time period. The server supports user interaction operations, such as debugging data signal presentation, zooming, dragging, and the like. An analyst can perform interactive operations such as selection, zooming, dragging and the like in the page, and further analyze details of abnormal problem data so as to analyze and obtain detailed problems causing the abnormality. In addition, if an analyst finds that an abnormal problem exists and needs to analyze when browsing the operation data of the unmanned vehicle in the operation process normally, the analyst can select an input time point analysis mode at the server, input corresponding start time and end time, acquire the operation data of the module to be detected in the time period, extract the abnormal problem data in the operation data, and analyze the reason of the abnormal problem.
In the embodiment of the present application, the debugging according to the abnormal problem data, and determining the reason for the abnormality according to the debugging result includes: simulating the running process of the vehicle according to the abnormal input data and the abnormal output data of the module to be detected by the vehicle model, and determining candidate abnormal reasons according to the vehicle simulation data; and determining a target abnormal reason from the candidate abnormal reasons according to the internal state variable data of the module to be detected by a web technology.
Illustratively, the running process of a macro playback vehicle is simulated according to the input and output data in the extracted abnormal problem data through a vehicle model, and candidate abnormal reasons generated by the abnormal problems are determined according to vehicle simulation data. In order to reduce the workload of debugging according to the internal state variable data, preferably, the module to be detected is debugged according to the internal state variable data associated with the candidate abnormality cause, that is, the internal state variable data for determining the candidate abnormality cause is loaded, and the internal state variable data is analyzed through interactive operation, for example, a graph of the internal state variable data is analyzed, so as to determine a specific detailed target abnormality cause according to an analysis result. By processing the recorded internal state variable data, fine-grained processing at a frame level is supported, and further determination of the target abnormal reason from the candidate abnormal reasons is facilitated.
The two debugging modes in the embodiment of the present application may be: and when the module to be detected is a control module, adopting the former scheme, and when the module to be detected is a local decision planning module, adopting the latter scheme. It should be noted that the two schemes are applicable to different modules to be detected, and an analyst can select and adopt the schemes according to actual conditions.
According to the technical scheme, the abnormal problem data are extracted from the operation data of the pre-recorded module to be detected through the server according to the abnormal problem information of the module to be detected, so that the more detailed abnormal problem data of the module to be detected can be acquired in a targeted manner, and further analysis is facilitated. The abnormal reason is determined according to the debugging result by debugging the abnormal problem data, so that the specific reason of the abnormal occurrence is accurately analyzed, and the efficiency of determining the abnormal reason is improved.
Fig. 2 is a schematic flow chart of another unmanned vehicle debugging method provided according to an embodiment of the present application. The present embodiment is an alternative proposed on the basis of the above-described embodiments. Referring to fig. 2, the unmanned vehicle debugging method provided in this embodiment includes:
s210, determining an abnormal time period and an abnormal data extraction template according to abnormal problem information of the module to be detected in the unmanned vehicle.
The abnormal time period is a time period in which an abnormal problem occurs, and may be a time period for reporting, or a time period selected by an analyst in an observation process and requiring abnormal problem analysis. Illustratively, an abnormal time point of the abnormal problem is determined, and a period of 30 seconds before and after the abnormal time point is taken as the abnormal period. Or acquiring a starting time point and an ending time point input by an analyst, and taking a time period formed by the starting time point and the ending time point as an abnormal time period. And when the reported abnormal problem exists, establishing a debugging environment, and extracting data according to the abnormal time period of the reported abnormal problem. And generating a link for the abnormal problem data of each frame, so that an analyst can conveniently enter an analysis environment when clicking the link, and the analysis environment loads the abnormal problem data corresponding to the abnormal problem. The abnormal data extraction template is a module which is predetermined and used for determining the data type required by the abnormal problem, and different modules to be detected or different control functions can correspond to different abnormal data extraction templates. According to the abnormal data extraction template, data required by abnormal problem analysis can be selected from dozens of data so as to perform the abnormal reason analysis in a targeted manner.
For example, before extracting the data, the data in the operation process of the module to be detected is stored. For example, the control algorithm module stores key debugging information data, stores key debugging information such as time points, transverse control errors, longitudinal control errors, transverse control error change rates, course angle errors, course angle error change rates, main vehicle positions and the like, and stores data such as cloud servers for indexing to construct data indexes of the debugging information. The local decision planning module, when printing and storing the debugging information, will index the used external dependent data sequence from the consideration of reproducing the whole environment information: such as perception data index, prediction data index, traffic light data index, path data index, vehicle chassis data index, positioning data index and the like, internal state variable data, some debugging data which is not easy to be repeatedly generated off line and the like, such as information of whether changing lanes, whether parking, road surface information sampling points and the like. If there are multiple threads that have computed the result, the data for the multiple threads may be stored. It may also be stored in a log format in a serialized binary or structured textual manner.
And S220, extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal time period and the data type in the abnormal data extraction template.
For example, according to an abnormal time period, abnormal problem data in the time period is determined, and abnormal problem data for performing abnormal problem analysis is selected from a plurality of types of data according to the data type in the abnormal problem data extraction template. When the module to be detected is a control module and the speed error needs to be analyzed, the data type determined in the abnormal data extraction template may include: driving mode, planned speed, planned acceleration, current speed, current acceleration, and acceleration control output, etc.
And S230, debugging according to the abnormal problem data, and determining an abnormal reason according to a debugging result.
For example, for displaying abnormal problem data by the server, the abnormal problem data may be played and loaded by taking a frame sequence of the module to be detected as a main axis when the server is loaded, and data corresponding to the index, such as perception data, prediction data, traffic light data, path data, vehicle chassis data, positioning data, and the like, is loaded. The internal state variable data and other data can be displayed in a new interface or a popped up map layer in a drawing way, such as drawing a map lane line, projecting obstacles, projecting predicted objects at discrete time points, constraining, feasible fields, finally solved curves, finally solved sampling information and the like. For the above data, layer information is focused on the macro layer analysis problem, and the visualization of the input information can be seen. For the debugging information in the algorithm, how the algorithm works in each frame can be observed in detail, so that the specific abnormal reason is analyzed.
In the embodiment of the application, in order to better analyze the problem, display schemes such as moving a frame forward and backward for displaying, moving an N frame forward and backward for displaying, and designating an nth frame for displaying are added, where N >0, which may be 10, for example. If a plurality of planning tasks are calculated in a calculation period in the local decision planning module, namely a plurality of path planning debugging information are stored in one frame of debugging information, correspondingly selected different path planning information is displayed during displaying, and the selected output path planning information is displayed by default.
According to the embodiment of the application, the abnormal relevant problems are extracted according to the abnormal time period and the abnormal data extraction template, so that abnormal problem data required by the abnormal reason analysis of the module to be detected can be determined more specifically, and specific abnormal reasons can be accurately analyzed. Through data display and interactive operation of the analysis environment, internal state variable data which cause abnormity are intuitively and clearly analyzed, and abnormal operation of the unmanned vehicle is specifically analyzed in a detailed mode. In addition, the server completes the whole process, so that the efficiency of determining the abnormal reason can be improved, the server does not need to determine the abnormal reason, the problem of inconsistency of the equipment end operating system is not considered, the problem is simplified, and the cost is saved. Compared with local system debugging, the method and the device can automatically construct an analysis environment, do not need complicated and repeated construction steps, and are convenient for directly analyzing abnormal reasons.
Fig. 3 is a schematic structural diagram of an unmanned vehicle debugging device provided according to an embodiment of the application. Referring to fig. 3, the present application discloses an unmanned vehicle commissioning apparatus 300, where the apparatus 300 includes: an abnormal problem data extraction module 301 and an abnormal cause determination module 302.
The abnormal problem data extraction module 301 is configured to extract abnormal problem data from operation data of a module to be detected, which is recorded in advance, according to abnormal problem information of the module to be detected in the unmanned vehicle;
an abnormal cause determining module 302, configured to perform debugging according to the abnormal problem data, and determine an abnormal cause according to a debugging result.
Further, the operation data of the module to be detected includes input data, internal state variable data and output data of the module to be detected.
Further, the abnormal problem data extraction module 301 includes:
the abnormal information determining unit is used for determining an abnormal time period and an abnormal data extraction template according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and the data extraction unit is used for extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal time period and the data type in the abnormal data extraction template.
Further, the abnormality cause determining module 302 includes:
the debugging unit is used for debugging according to the abnormal problem data through a web technology;
and the debugging result display unit is used for displaying the debugging result through a visual tool.
Further, the module 302 for determining the abnormality cause further includes:
the preliminary determining unit is used for simulating the running process of the vehicle according to the abnormal input data and the abnormal output data of the module to be detected through the vehicle model and determining candidate abnormal reasons according to vehicle simulation data;
and the reason determining unit is used for determining a target abnormal reason from the candidate abnormal reasons according to the internal state variable data of the module to be detected through a web technology.
Further, if the module to be detected is a control module, the input data includes: the output path, the positioning information and the vehicle chassis feedback information planned by the local decision planning module; the output data includes: a steering wheel angle; the internal state variable data includes: the grade value, the disturbance rejection value, the time, the lateral control error, the longitudinal control error, the rate of change of the lateral control error, the course angle error, the rate of change of the course angle error, and the position of the host vehicle.
Further, if the module to be detected is a local decision planning module, the input data includes: positioning data, map data, navigation data, vehicle chassis data, perception module data and prediction module data; the output data comprises a local trajectory; the internal state variable data includes: path planning variables, speed planning variables, and decision planning variables.
The unmanned vehicle debugging device provided by the embodiment of the application can execute the unmanned vehicle debugging method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
According to an embodiment of the present application, a server and a readable storage medium are also provided.
As shown in fig. 4, fig. 4 is a block diagram of a server for implementing the unmanned vehicle debugging method according to the embodiment of the present application. Server is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The server may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable servers, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 4, the server includes: one or more processors 401, memory 402, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executed within the server, including instructions stored in or on the memory to display graphical information of the GUI on an external input/output device (such as a display server coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as an array of servers, a group of blade servers, or a multi-processor system). In fig. 4, one processor 401 is taken as an example.
The memory 402, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of unmanned vehicle debugging in the embodiments of the present application (e.g., the abnormal problem data extraction module 301 and the abnormal cause determination module 302 shown in fig. 4). The processor 401 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 402, that is, implements the unmanned vehicle debugging method in the above-described method embodiment.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the unmanned vehicle-commissioned server, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to the drone vehicle commissioning server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The server of the unmanned vehicle debugging method may further include: an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403 and the output device 404 may be connected by a bus or other means, and fig. 4 illustrates an example of a connection by a bus.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the unmanned vehicle-commissioned server, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 404 may include a display server, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display server may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display server can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, server, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. An unmanned vehicle commissioning method, performed by a server, the method comprising:
extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and debugging according to the abnormal problem data, and determining the abnormal reason according to the debugging result.
2. The method according to claim 1, characterized in that the operational data of the module to be tested comprises input data, internal state variable data and output data of the module to be tested.
3. The method according to claim 1, wherein extracting abnormal problem data from the operation data of the module to be detected recorded in advance according to the abnormal problem information of the module to be detected in the unmanned vehicle comprises:
determining an abnormal time period and an abnormal data extraction template according to abnormal problem information of a module to be detected in the unmanned vehicle;
and extracting abnormal problem data from the pre-recorded operation data of the module to be detected according to the abnormal time period and the data type in the abnormal data extraction template.
4. The method of claim 1, wherein debugging according to the abnormal problem data comprises:
debugging according to the abnormal problem data through a web technology;
and displaying the debugging result through a visualization tool.
5. The method of claim 2, wherein debugging according to the abnormal problem data and determining the cause of the abnormality according to the debugging result comprises:
simulating the running process of the vehicle according to the abnormal input data and the abnormal output data of the module to be detected by the vehicle model, and determining candidate abnormal reasons according to the vehicle simulation data;
and determining a target abnormal reason from the candidate abnormal reasons according to the internal state variable data of the module to be detected by a web technology.
6. The method according to claim 2, wherein if the module to be detected is a control module, the input data comprises: the output path, the positioning information and the vehicle chassis feedback information planned by the local decision planning module; the output data includes: a steering wheel angle; the internal state variable data includes: the grade value, the disturbance rejection value, the time, the lateral control error, the longitudinal control error, the rate of change of the lateral control error, the course angle error, the rate of change of the course angle error, and the position of the host vehicle.
7. The method according to claim 2, wherein if the module to be detected is a local decision planning module, the input data comprises: positioning data, map data, navigation data, vehicle chassis data, perception module data and prediction module data; the output data comprises a local trajectory; the internal state variable data includes: path planning variables, speed planning variables, and decision planning variables.
8. An unmanned vehicle commissioning device, the device comprising:
the abnormal problem data extraction module is used for extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and the abnormal reason determining module is used for debugging according to the abnormal problem data and determining the abnormal reason according to the debugging result.
9. The apparatus of claim 8, wherein the abnormal problem data extraction module comprises:
the abnormal information determining unit is used for determining an abnormal time period and an abnormal data extraction template according to the abnormal problem information of the module to be detected in the unmanned vehicle;
and the data extraction unit is used for extracting abnormal problem data from the operation data of the module to be detected, which is recorded in advance, according to the abnormal time period and the data type in the abnormal data extraction template.
10. An unmanned vehicle debugging server, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
11. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
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